




'''simple docstring'''





import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


_SCREAMING_SNAKE_CASE				:  Union[str, Any] 						=					{
    "n_samples": 64,
    "horizon": 32,
    "num_inference_steps": 20,
    "n_guide_steps": 2,  # can set to 0 for faster sampling, does not use value network
    "scale_grad_by_std": True,
    "scale": 0.1,
    "eta": 0.0,
    "t_grad_cutoff": 2,
    "device": "cpu",
}


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE				:  Optional[int] 						=					"hopper-medium-v2"
		_SCREAMING_SNAKE_CASE				:  Dict 						=					gym.make(env_name)

		_SCREAMING_SNAKE_CASE				:  List[Any] 						=					ValueGuidedRLPipeline.from_pretrained(
		    "bglick13/hopper-medium-v2-value-function-hor32",
		    env=env,
		)

		env.seed(0)
		_SCREAMING_SNAKE_CASE				:  Union[str, Any] 						=					env.reset()
		_SCREAMING_SNAKE_CASE				:  Union[str, Any] 						=					0
		_SCREAMING_SNAKE_CASE				:  Optional[Any] 						=					0
		_SCREAMING_SNAKE_CASE				:  Optional[Any] 						=					1000
		_SCREAMING_SNAKE_CASE				:  Union[str, Any] 						=					[obs.copy()]
		try:
				for t in tqdm.tqdm(range(T)):
						# call the policy
						_SCREAMING_SNAKE_CASE				:  str 						=					pipeline(obs, planning_horizon=32)

						# execute action in environment
						_SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE				:  List[str] 						=					env.step(denorm_actions)
						_SCREAMING_SNAKE_CASE				:  List[Any] 						=					env.get_normalized_score(total_reward)

						# update return
						total_reward += reward
						total_score += score
						print(
						    F"Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:"
						    F" {total_score}"
						)

						# save observations for rendering
						rollout.append(next_observation.copy())

						_SCREAMING_SNAKE_CASE				:  Optional[int] 						=					next_observation
		except KeyboardInterrupt:
				pass

		print(F"Total reward: {total_reward}")











"""simple docstring"""





def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		int				,		SCREAMING_SNAKE_CASE	:		int     ):







      '''simple docstring'''




      if a < 0 or b < 0:
            raise ValueError("""the value of both inputs must be positive"""     )
      lowerCAmelCase    		=			str(bin(SCREAMING_SNAKE_CASE     )     )[2:]  # remove the leading "0b"
      lowerCAmelCase    		=			str(bin(SCREAMING_SNAKE_CASE     )     )[2:]
      lowerCAmelCase    		=			max(len(SCREAMING_SNAKE_CASE     )				,		len(SCREAMING_SNAKE_CASE     )     )
      return "0b" + "".join(
          str(int("""1""" in (char_a, char_b)     )     )
          for char_a, char_b in zip(a_binary.zfill(SCREAMING_SNAKE_CASE     )				,		b_binary.zfill(SCREAMING_SNAKE_CASE     )     )     )


if __name__ == "__main__":
       import doctest

       doctest.testmod()






